356 lines
16 KiB
C
356 lines
16 KiB
C
|
/*
|
||
|
|
* Software License Agreement (BSD License)
|
||
|
|
*
|
||
|
|
* Point Cloud Library (PCL) - www.pointclouds.org
|
||
|
|
* Copyright (c) 2009-2012, Willow Garage, Inc.
|
||
|
|
* Copyright (c) 2012-, Open Perception, Inc.
|
||
|
|
*
|
||
|
|
* All rights reserved.
|
||
|
|
*
|
||
|
|
* Redistribution and use in source and binary forms, with or without
|
||
|
|
* modification, are permitted provided that the following conditions
|
||
|
|
* are met:
|
||
|
|
*
|
||
|
|
* * Redistributions of source code must retain the above copyright
|
||
|
|
* notice, this list of conditions and the following disclaimer.
|
||
|
|
* * Redistributions in binary form must reproduce the above
|
||
|
|
* copyright notice, this list of conditions and the following
|
||
|
|
* disclaimer in the documentation and/or other materials provided
|
||
|
|
* with the distribution.
|
||
|
|
* * Neither the name of the copyright holder(s) nor the names of its
|
||
|
|
* contributors may be used to endorse or promote products derived
|
||
|
|
* from this software without specific prior written permission.
|
||
|
|
*
|
||
|
|
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
|
||
|
|
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
|
||
|
|
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
|
||
|
|
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
|
||
|
|
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
|
||
|
|
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
|
||
|
|
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
|
||
|
|
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||
|
|
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
|
||
|
|
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
|
||
|
|
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
||
|
|
* POSSIBILITY OF SUCH DAMAGE.
|
||
|
|
*
|
||
|
|
*/
|
||
|
|
|
||
|
|
#pragma once
|
||
|
|
|
||
|
|
#include <pcl/sample_consensus/sac_model.h>
|
||
|
|
#include <pcl/sample_consensus/model_types.h>
|
||
|
|
#include <pcl/common/distances.h>
|
||
|
|
|
||
|
|
namespace pcl
|
||
|
|
{
|
||
|
|
/** \brief @b SampleConsensusModelCone defines a model for 3D cone segmentation.
|
||
|
|
* The model coefficients are defined as:
|
||
|
|
* <ul>
|
||
|
|
* <li><b>apex.x</b> : the X coordinate of cone's apex
|
||
|
|
* <li><b>apex.y</b> : the Y coordinate of cone's apex
|
||
|
|
* <li><b>apex.z</b> : the Z coordinate of cone's apex
|
||
|
|
* <li><b>axis_direction.x</b> : the X coordinate of the cone's axis direction
|
||
|
|
* <li><b>axis_direction.y</b> : the Y coordinate of the cone's axis direction
|
||
|
|
* <li><b>axis_direction.z</b> : the Z coordinate of the cone's axis direction
|
||
|
|
* <li><b>opening_angle</b> : the cone's opening angle
|
||
|
|
* </ul>
|
||
|
|
* \author Stefan Schrandt
|
||
|
|
* \ingroup sample_consensus
|
||
|
|
*/
|
||
|
|
template <typename PointT, typename PointNT>
|
||
|
|
class SampleConsensusModelCone : public SampleConsensusModel<PointT>, public SampleConsensusModelFromNormals<PointT, PointNT>
|
||
|
|
{
|
||
|
|
public:
|
||
|
|
using SampleConsensusModel<PointT>::model_name_;
|
||
|
|
using SampleConsensusModel<PointT>::input_;
|
||
|
|
using SampleConsensusModel<PointT>::indices_;
|
||
|
|
using SampleConsensusModel<PointT>::radius_min_;
|
||
|
|
using SampleConsensusModel<PointT>::radius_max_;
|
||
|
|
using SampleConsensusModelFromNormals<PointT, PointNT>::normals_;
|
||
|
|
using SampleConsensusModelFromNormals<PointT, PointNT>::normal_distance_weight_;
|
||
|
|
using SampleConsensusModel<PointT>::error_sqr_dists_;
|
||
|
|
|
||
|
|
using PointCloud = typename SampleConsensusModel<PointT>::PointCloud;
|
||
|
|
using PointCloudPtr = typename SampleConsensusModel<PointT>::PointCloudPtr;
|
||
|
|
using PointCloudConstPtr = typename SampleConsensusModel<PointT>::PointCloudConstPtr;
|
||
|
|
|
||
|
|
using Ptr = shared_ptr<SampleConsensusModelCone<PointT, PointNT> >;
|
||
|
|
using ConstPtr = shared_ptr<const SampleConsensusModelCone<PointT, PointNT>>;
|
||
|
|
|
||
|
|
/** \brief Constructor for base SampleConsensusModelCone.
|
||
|
|
* \param[in] cloud the input point cloud dataset
|
||
|
|
* \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
|
||
|
|
*/
|
||
|
|
SampleConsensusModelCone (const PointCloudConstPtr &cloud, bool random = false)
|
||
|
|
: SampleConsensusModel<PointT> (cloud, random)
|
||
|
|
, SampleConsensusModelFromNormals<PointT, PointNT> ()
|
||
|
|
, axis_ (Eigen::Vector3f::Zero ())
|
||
|
|
, eps_angle_ (0)
|
||
|
|
, min_angle_ (-std::numeric_limits<double>::max ())
|
||
|
|
, max_angle_ (std::numeric_limits<double>::max ())
|
||
|
|
{
|
||
|
|
model_name_ = "SampleConsensusModelCone";
|
||
|
|
sample_size_ = 3;
|
||
|
|
model_size_ = 7;
|
||
|
|
}
|
||
|
|
|
||
|
|
/** \brief Constructor for base SampleConsensusModelCone.
|
||
|
|
* \param[in] cloud the input point cloud dataset
|
||
|
|
* \param[in] indices a vector of point indices to be used from \a cloud
|
||
|
|
* \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
|
||
|
|
*/
|
||
|
|
SampleConsensusModelCone (const PointCloudConstPtr &cloud,
|
||
|
|
const Indices &indices,
|
||
|
|
bool random = false)
|
||
|
|
: SampleConsensusModel<PointT> (cloud, indices, random)
|
||
|
|
, SampleConsensusModelFromNormals<PointT, PointNT> ()
|
||
|
|
, axis_ (Eigen::Vector3f::Zero ())
|
||
|
|
, eps_angle_ (0)
|
||
|
|
, min_angle_ (-std::numeric_limits<double>::max ())
|
||
|
|
, max_angle_ (std::numeric_limits<double>::max ())
|
||
|
|
{
|
||
|
|
model_name_ = "SampleConsensusModelCone";
|
||
|
|
sample_size_ = 3;
|
||
|
|
model_size_ = 7;
|
||
|
|
}
|
||
|
|
|
||
|
|
/** \brief Copy constructor.
|
||
|
|
* \param[in] source the model to copy into this
|
||
|
|
*/
|
||
|
|
SampleConsensusModelCone (const SampleConsensusModelCone &source) :
|
||
|
|
SampleConsensusModel<PointT> (),
|
||
|
|
SampleConsensusModelFromNormals<PointT, PointNT> (),
|
||
|
|
eps_angle_ (), min_angle_ (), max_angle_ ()
|
||
|
|
{
|
||
|
|
*this = source;
|
||
|
|
model_name_ = "SampleConsensusModelCone";
|
||
|
|
}
|
||
|
|
|
||
|
|
/** \brief Empty destructor */
|
||
|
|
~SampleConsensusModelCone () {}
|
||
|
|
|
||
|
|
/** \brief Copy constructor.
|
||
|
|
* \param[in] source the model to copy into this
|
||
|
|
*/
|
||
|
|
inline SampleConsensusModelCone&
|
||
|
|
operator = (const SampleConsensusModelCone &source)
|
||
|
|
{
|
||
|
|
SampleConsensusModel<PointT>::operator=(source);
|
||
|
|
SampleConsensusModelFromNormals<PointT, PointNT>::operator=(source);
|
||
|
|
axis_ = source.axis_;
|
||
|
|
eps_angle_ = source.eps_angle_;
|
||
|
|
min_angle_ = source.min_angle_;
|
||
|
|
max_angle_ = source.max_angle_;
|
||
|
|
return (*this);
|
||
|
|
}
|
||
|
|
|
||
|
|
/** \brief Set the angle epsilon (delta) threshold.
|
||
|
|
* \param[in] ea the maximum allowed difference between the cone's axis and the given axis.
|
||
|
|
*/
|
||
|
|
inline void
|
||
|
|
setEpsAngle (double ea) { eps_angle_ = ea; }
|
||
|
|
|
||
|
|
/** \brief Get the angle epsilon (delta) threshold. */
|
||
|
|
inline double
|
||
|
|
getEpsAngle () const { return (eps_angle_); }
|
||
|
|
|
||
|
|
/** \brief Set the axis along which we need to search for a cone direction.
|
||
|
|
* \param[in] ax the axis along which we need to search for a cone direction
|
||
|
|
*/
|
||
|
|
inline void
|
||
|
|
setAxis (const Eigen::Vector3f &ax) { axis_ = ax; }
|
||
|
|
|
||
|
|
/** \brief Get the axis along which we need to search for a cone direction. */
|
||
|
|
inline Eigen::Vector3f
|
||
|
|
getAxis () const { return (axis_); }
|
||
|
|
|
||
|
|
/** \brief Set the minimum and maximum allowable opening angle for a cone model
|
||
|
|
* given from a user.
|
||
|
|
* \param[in] min_angle the minimum allowable opening angle of a cone model
|
||
|
|
* \param[in] max_angle the maximum allowable opening angle of a cone model
|
||
|
|
*/
|
||
|
|
inline void
|
||
|
|
setMinMaxOpeningAngle (const double &min_angle, const double &max_angle)
|
||
|
|
{
|
||
|
|
min_angle_ = min_angle;
|
||
|
|
max_angle_ = max_angle;
|
||
|
|
}
|
||
|
|
|
||
|
|
/** \brief Get the opening angle which we need minimum to validate a cone model.
|
||
|
|
* \param[out] min_angle the minimum allowable opening angle of a cone model
|
||
|
|
* \param[out] max_angle the maximum allowable opening angle of a cone model
|
||
|
|
*/
|
||
|
|
inline void
|
||
|
|
getMinMaxOpeningAngle (double &min_angle, double &max_angle) const
|
||
|
|
{
|
||
|
|
min_angle = min_angle_;
|
||
|
|
max_angle = max_angle_;
|
||
|
|
}
|
||
|
|
|
||
|
|
/** \brief Check whether the given index samples can form a valid cone model, compute the model coefficients
|
||
|
|
* from these samples and store them in model_coefficients. The cone coefficients are: apex,
|
||
|
|
* axis_direction, opening_angle.
|
||
|
|
* \param[in] samples the point indices found as possible good candidates for creating a valid model
|
||
|
|
* \param[out] model_coefficients the resultant model coefficients
|
||
|
|
*/
|
||
|
|
bool
|
||
|
|
computeModelCoefficients (const Indices &samples,
|
||
|
|
Eigen::VectorXf &model_coefficients) const override;
|
||
|
|
|
||
|
|
/** \brief Compute all distances from the cloud data to a given cone model.
|
||
|
|
* \param[in] model_coefficients the coefficients of a cone model that we need to compute distances to
|
||
|
|
* \param[out] distances the resultant estimated distances
|
||
|
|
*/
|
||
|
|
void
|
||
|
|
getDistancesToModel (const Eigen::VectorXf &model_coefficients,
|
||
|
|
std::vector<double> &distances) const override;
|
||
|
|
|
||
|
|
/** \brief Select all the points which respect the given model coefficients as inliers.
|
||
|
|
* \param[in] model_coefficients the coefficients of a cone model that we need to compute distances to
|
||
|
|
* \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
|
||
|
|
* \param[out] inliers the resultant model inliers
|
||
|
|
*/
|
||
|
|
void
|
||
|
|
selectWithinDistance (const Eigen::VectorXf &model_coefficients,
|
||
|
|
const double threshold,
|
||
|
|
Indices &inliers) override;
|
||
|
|
|
||
|
|
/** \brief Count all the points which respect the given model coefficients as inliers.
|
||
|
|
*
|
||
|
|
* \param[in] model_coefficients the coefficients of a model that we need to compute distances to
|
||
|
|
* \param[in] threshold maximum admissible distance threshold for determining the inliers from the outliers
|
||
|
|
* \return the resultant number of inliers
|
||
|
|
*/
|
||
|
|
std::size_t
|
||
|
|
countWithinDistance (const Eigen::VectorXf &model_coefficients,
|
||
|
|
const double threshold) const override;
|
||
|
|
|
||
|
|
|
||
|
|
/** \brief Recompute the cone coefficients using the given inlier set and return them to the user.
|
||
|
|
* @note: these are the coefficients of the cone model after refinement (e.g. after SVD)
|
||
|
|
* \param[in] inliers the data inliers found as supporting the model
|
||
|
|
* \param[in] model_coefficients the initial guess for the optimization
|
||
|
|
* \param[out] optimized_coefficients the resultant recomputed coefficients after non-linear optimization
|
||
|
|
*/
|
||
|
|
void
|
||
|
|
optimizeModelCoefficients (const Indices &inliers,
|
||
|
|
const Eigen::VectorXf &model_coefficients,
|
||
|
|
Eigen::VectorXf &optimized_coefficients) const override;
|
||
|
|
|
||
|
|
|
||
|
|
/** \brief Create a new point cloud with inliers projected onto the cone model.
|
||
|
|
* \param[in] inliers the data inliers that we want to project on the cone model
|
||
|
|
* \param[in] model_coefficients the coefficients of a cone model
|
||
|
|
* \param[out] projected_points the resultant projected points
|
||
|
|
* \param[in] copy_data_fields set to true if we need to copy the other data fields
|
||
|
|
*/
|
||
|
|
void
|
||
|
|
projectPoints (const Indices &inliers,
|
||
|
|
const Eigen::VectorXf &model_coefficients,
|
||
|
|
PointCloud &projected_points,
|
||
|
|
bool copy_data_fields = true) const override;
|
||
|
|
|
||
|
|
/** \brief Verify whether a subset of indices verifies the given cone model coefficients.
|
||
|
|
* \param[in] indices the data indices that need to be tested against the cone model
|
||
|
|
* \param[in] model_coefficients the cone model coefficients
|
||
|
|
* \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
|
||
|
|
*/
|
||
|
|
bool
|
||
|
|
doSamplesVerifyModel (const std::set<index_t> &indices,
|
||
|
|
const Eigen::VectorXf &model_coefficients,
|
||
|
|
const double threshold) const override;
|
||
|
|
|
||
|
|
/** \brief Return a unique id for this model (SACMODEL_CONE). */
|
||
|
|
inline pcl::SacModel
|
||
|
|
getModelType () const override { return (SACMODEL_CONE); }
|
||
|
|
|
||
|
|
protected:
|
||
|
|
using SampleConsensusModel<PointT>::sample_size_;
|
||
|
|
using SampleConsensusModel<PointT>::model_size_;
|
||
|
|
|
||
|
|
/** \brief Get the distance from a point to a line (represented by a point and a direction)
|
||
|
|
* \param[in] pt a point
|
||
|
|
* \param[in] model_coefficients the line coefficients (a point on the line, line direction)
|
||
|
|
*/
|
||
|
|
double
|
||
|
|
pointToAxisDistance (const Eigen::Vector4f &pt, const Eigen::VectorXf &model_coefficients) const;
|
||
|
|
|
||
|
|
/** \brief Check whether a model is valid given the user constraints.
|
||
|
|
* \param[in] model_coefficients the set of model coefficients
|
||
|
|
*/
|
||
|
|
bool
|
||
|
|
isModelValid (const Eigen::VectorXf &model_coefficients) const override;
|
||
|
|
|
||
|
|
/** \brief Check if a sample of indices results in a good sample of points
|
||
|
|
* indices. Pure virtual.
|
||
|
|
* \param[in] samples the resultant index samples
|
||
|
|
*/
|
||
|
|
bool
|
||
|
|
isSampleGood (const Indices &samples) const override;
|
||
|
|
|
||
|
|
private:
|
||
|
|
/** \brief The axis along which we need to search for a cone direction. */
|
||
|
|
Eigen::Vector3f axis_;
|
||
|
|
|
||
|
|
/** \brief The maximum allowed difference between the cone direction and the given axis. */
|
||
|
|
double eps_angle_;
|
||
|
|
|
||
|
|
/** \brief The minimum and maximum allowed opening angles of valid cone model. */
|
||
|
|
double min_angle_;
|
||
|
|
double max_angle_;
|
||
|
|
|
||
|
|
/** \brief Functor for the optimization function */
|
||
|
|
struct OptimizationFunctor : pcl::Functor<float>
|
||
|
|
{
|
||
|
|
/** Functor constructor
|
||
|
|
* \param[in] indices the indices of data points to evaluate
|
||
|
|
* \param[in] estimator pointer to the estimator object
|
||
|
|
*/
|
||
|
|
OptimizationFunctor (const pcl::SampleConsensusModelCone<PointT, PointNT> *model, const Indices& indices) :
|
||
|
|
pcl::Functor<float> (indices.size ()), model_ (model), indices_ (indices) {}
|
||
|
|
|
||
|
|
/** Cost function to be minimized
|
||
|
|
* \param[in] x variables array
|
||
|
|
* \param[out] fvec resultant functions evaluations
|
||
|
|
* \return 0
|
||
|
|
*/
|
||
|
|
int
|
||
|
|
operator() (const Eigen::VectorXf &x, Eigen::VectorXf &fvec) const
|
||
|
|
{
|
||
|
|
Eigen::Vector4f apex (x[0], x[1], x[2], 0);
|
||
|
|
Eigen::Vector4f axis_dir (x[3], x[4], x[5], 0);
|
||
|
|
float opening_angle = x[6];
|
||
|
|
|
||
|
|
float apexdotdir = apex.dot (axis_dir);
|
||
|
|
float dirdotdir = 1.0f / axis_dir.dot (axis_dir);
|
||
|
|
|
||
|
|
for (int i = 0; i < values (); ++i)
|
||
|
|
{
|
||
|
|
// dist = f - r
|
||
|
|
Eigen::Vector4f pt = (*model_->input_)[indices_[i]].getVector4fMap();
|
||
|
|
pt[3] = 0;
|
||
|
|
|
||
|
|
// Calculate the point's projection on the cone axis
|
||
|
|
float k = (pt.dot (axis_dir) - apexdotdir) * dirdotdir;
|
||
|
|
Eigen::Vector4f pt_proj = apex + k * axis_dir;
|
||
|
|
|
||
|
|
// Calculate the actual radius of the cone at the level of the projected point
|
||
|
|
Eigen::Vector4f height = apex-pt_proj;
|
||
|
|
float actual_cone_radius = tanf (opening_angle) * height.norm ();
|
||
|
|
|
||
|
|
fvec[i] = static_cast<float> (pcl::sqrPointToLineDistance (pt, apex, axis_dir) - actual_cone_radius * actual_cone_radius);
|
||
|
|
}
|
||
|
|
return (0);
|
||
|
|
}
|
||
|
|
|
||
|
|
const pcl::SampleConsensusModelCone<PointT, PointNT> *model_;
|
||
|
|
const Indices &indices_;
|
||
|
|
};
|
||
|
|
};
|
||
|
|
}
|
||
|
|
|
||
|
|
#ifdef PCL_NO_PRECOMPILE
|
||
|
|
#include <pcl/sample_consensus/impl/sac_model_cone.hpp>
|
||
|
|
#endif
|